Clinical tools to accurately describe, evaluate and predict an individual's response to cancer therapy are a field-wide priority. For example, the adjuvant treatment of women with early stage breast cancer will subject a significantly larger number of women to chemotherapy than who can benefit from it, or in the case of advanced cancers only 10-20 percent of individuals will have a clinical benefit from therapy, yet we treat the entire population. Furthermore, many therapies are initially effective, but lose effectiveness over time. Under the current paradigm, diseases are first classified into one of a small number of groups and then an indicated treatment is attempted. These groups have poor resolution and frequently poor predictive power leaving oncologists with limited means to assess or predict a patient's response to a given therapy. Instead, patients must undergo therapy that is often not optimal for their disease only to have it fail so that yet another therapeutic regimen must be attempted.
An integrative, multi-scale approach is necessary to develop accurate, useable models to study complex diseases such as cancer. Combining diverse perspectives and techniques developed by physical and quantitative sciences with that of biological sciences can make a significant impact on cancer management and treatment in revolutionary ways. Subtle molecular-scale perturbations (e.g. mutation in a gene) can produce dramatic, tumor-scale (e.g. invasiveness) and organism scale (e.g. responsiveness to therapy) affects. To generate a model of cancer that can be used to predict the behavior of cancers during emergence and in response to perturbation, diverse physical measurements at multiple scales from molecular to organismic level need to be integrated with sophisticated and diverse modeling approaches. Multi-scale measurement and modeling would allow specific cases of cancer to be modeled with sufficient fidelity to estimate the relative probable efficacy of alternative therapies (e.g., by rationally integrating genotype, tumor environment and treatment parameters to predict outcome). When integrated in an appropriate modeling framework, analyses of specific properties at the molecular-cell, tumor and host levels can inform therapeutic response.